Prostate cancer is the most common cancer diagnosed in men in Canada. Magnetic resonance imaging (MRI) may become a valuable tool to non-invasively identify prostate cancer and assess its biological aggressiveness, which in turn will help doctors make better decisions about how to treat an individual patient's prostate cancer.

Despite the promise of MRI for detecting and characterizing prostate cancer, there are several recognized limitations and challenges. These include lack of standardized interpretation and reporting of prostate MRI exams.

The investigators propose to validate and improve a computer program computerized prediction tool that will use information from MR images to inform us how aggressive a prostate cancer is. The hypothesis is that this computer-aided approach will increase the reproducibility and accuracy of MRI in predicting the tumor biology information about the imaged prostate cancer.

Prostate biopsies are the gold standard assessment of how prostate cancer is diagnosed and how low risk prostate cancers are surveilled. The investigators have produced a machine-learning based algorithm which uses MRI characteristics (radiomic features or textures) to predict the results of a prostate biopsy. The field has numerous concerns that such radiomic based predictions will not be reproducible, as there as so many subtle changes between MRI scans of different patients.

The interventions are the use of the MRT and the use of a second MRI of the prostate (MRI-P).

Two primary outcomes will be investigated. First, the existing radiomics predictive model, labeled as the MRI-P based Radiomics Tool (MRT) will predict the Grade Group (GG) and compare it to the gold standard, pathologist's evaluation of the Grade Group (GG). Second, the stability of the predicted GG between two shortly spaced MRI-Ps will be compared.

Patients with a detectable prostate nodule on MRI-P which localizes to a biopsy confirmed prostate cancer will be approached for enrollment. If enrolled, participants will attend for a subsequent MRI-P in a brief time frame relative to the acquisition of the first MRI-P. Attempts will be made to obtain participants that allow for even distribution among all GGs.

Inclusion Criteria:

An appropriate diagnostic MRI-P, defined as:

• Being performed on 3T MRI at the Halifax Infirmary Building
• Taken place within 5 weeks of study enrolment
• Having a detectable nodule which anatomically localizes to prostate cancer (PCa) identified in diagnostic biopsy specimen
• Acquired T1+contrast, T2, and attenuated diffusion coefficient (ADC) series axial images of the prostate

An appropriate diagnostic biopsy, defined as:

• Taken place within 2 months of the participant's MRI-P 1
• Taken place within 3 months of participant's study enrolment
• Reports diagnosis of PCa
• Reports a systematic assessment of the biopsy, assessing at least 12 cores
• Reports at least on core involved with PCa and this core must anatomically localise to a nodule seen on MRI-P 1

Exclusion Criteria:

• Past prostatic interventions which would influence the prostate's structure
• Alterations to physiological testosterone levels
• Inability to position one's self in a reproducible fashion for an MRI-P
• Patient factors reported to produce significant artifact on MRI-P 1

Will be reported at study completion, expected in 2023.

The study protocol and SAP will be shared in the publication. Analytic code and images will be shared with collaborating institutions and groups that have agreed to a data sharing agreement with the investigators.